The use of artificial intelligence (AI) is becoming more prevalent across industries such as
healthcare, finance, and transportation. Artificial intelligence is based on the analysis of …

The ubiquity of data lakes has created fascinating new challenges for data management
research. In this tutorial, we review the state-of-the-art in data management for data lakes …

Data-centric AI is at the center of a fundamental shift in software engineering where machine
learning becomes the new software, powered by big data and computing infrastructure …

Data collection is a major bottleneck in machine learning and an active research topic in
multiple communities. There are largely two reasons data collection has recently become a …

Over the past two decades, we have witnessed an exponential increase of data production
in the world. So-called big data generally come from transactional systems, and even more …

There are thousands of data repositories on the Web, providing access to millions of
datasets. National and regional governments, scientific publishers and consortia …

With the continuous and vast increase in the amount of data in our digital world, it has been
acknowledged that the number of knowledgeable data scientists can not scale to address …

Generating value from data requires the ability to find, access and make sense of datasets.
There are many efforts underway to encourage data sharing and reuse, from scientific …

Organizations face a data discovery problem when their analysts spend more time looking
for relevant data than analyzing it. This problem has become commonplace in modern …

Modern companies and institutions rely on data to guide every single business process and
decision. Missing or incorrect information seriously compromises any decision process …